Overview

Brought to you by YData

Dataset statistics

Number of variables8
Number of observations13605401
Missing cells5810
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory493.1 MiB
Average record size in memory38.0 B

Variable types

Numeric8

Alerts

AMT_INSTALMENT is highly overall correlated with AMT_PAYMENT and 1 other fieldsHigh correlation
AMT_PAYMENT is highly overall correlated with AMT_INSTALMENTHigh correlation
DAYS_ENTRY_PAYMENT is highly overall correlated with DAYS_INSTALMENTHigh correlation
DAYS_INSTALMENT is highly overall correlated with DAYS_ENTRY_PAYMENTHigh correlation
NUM_INSTALMENT_NUMBER is highly overall correlated with NUM_INSTALMENT_VERSIONHigh correlation
NUM_INSTALMENT_VERSION is highly overall correlated with AMT_INSTALMENT and 1 other fieldsHigh correlation
NUM_INSTALMENT_VERSION has 4082498 (30.0%) zeros Zeros

Reproduction

Analysis started2025-03-25 03:08:49.705801
Analysis finished2025-03-25 03:13:04.225502
Duration4 minutes and 14.52 seconds
Software versionydata-profiling vv4.15.1
Download configurationconfig.json

Variables

SK_ID_PREV
Real number (ℝ)

Distinct997752
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1903365
Minimum1000001
Maximum2843499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.9 MiB
2025-03-25T05:13:04.289718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1000001
5-th percentile1082903
Q11434191
median1896520
Q32369094
95-th percentile2749692
Maximum2843499
Range1843498
Interquartile range (IQR)934903

Descriptive statistics

Standard deviation536202.91
Coefficient of variation (CV)0.28171313
Kurtosis-1.2171478
Mean1903365
Median Absolute Deviation (MAD)467405
Skewness0.042509604
Sum2.5896044 × 1013
Variance2.8751356 × 1011
MonotonicityNot monotonic
2025-03-25T05:13:04.397805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2360056 293
 
< 0.1%
2592574 279
 
< 0.1%
1017477 248
 
< 0.1%
1449382 243
 
< 0.1%
1746731 236
 
< 0.1%
1690678 223
 
< 0.1%
2709164 222
 
< 0.1%
1383111 220
 
< 0.1%
1152155 219
 
< 0.1%
2543266 216
 
< 0.1%
Other values (997742) 13603002
> 99.9%
ValueCountFrequency (%)
1000001 2
 
< 0.1%
1000002 4
 
< 0.1%
1000003 3
 
< 0.1%
1000004 7
< 0.1%
1000005 11
< 0.1%
1000007 5
< 0.1%
1000008 9
< 0.1%
1000009 6
< 0.1%
1000010 11
< 0.1%
1000011 12
< 0.1%
ValueCountFrequency (%)
2843499 10
 
< 0.1%
2843498 6
 
< 0.1%
2843497 20
< 0.1%
2843496 34
< 0.1%
2843495 7
 
< 0.1%
2843494 2
 
< 0.1%
2843493 31
< 0.1%
2843492 12
 
< 0.1%
2843491 10
 
< 0.1%
2843490 4
 
< 0.1%

SK_ID_CURR
Real number (ℝ)

Distinct339587
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean278444.88
Minimum100001
Maximum456255
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.9 MiB
2025-03-25T05:13:04.475927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum100001
5-th percentile118148
Q1189639
median278685
Q3367530
95-th percentile438474
Maximum456255
Range356254
Interquartile range (IQR)177891

Descriptive statistics

Standard deviation102718.31
Coefficient of variation (CV)0.36889998
Kurtosis-1.1970155
Mean278444.88
Median Absolute Deviation (MAD)88956
Skewness-0.003354141
Sum3.7883543 × 1012
Variance1.0551051 × 1010
MonotonicityNot monotonic
2025-03-25T05:13:04.554903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
145728 372
 
< 0.1%
296205 350
 
< 0.1%
453103 347
 
< 0.1%
189699 344
 
< 0.1%
186851 337
 
< 0.1%
172690 336
 
< 0.1%
418081 332
 
< 0.1%
192083 324
 
< 0.1%
434807 323
 
< 0.1%
209362 318
 
< 0.1%
Other values (339577) 13602018
> 99.9%
ValueCountFrequency (%)
100001 7
 
< 0.1%
100002 19
 
< 0.1%
100003 25
 
< 0.1%
100004 3
 
< 0.1%
100005 9
 
< 0.1%
100006 16
 
< 0.1%
100007 66
< 0.1%
100008 35
< 0.1%
100009 51
< 0.1%
100010 10
 
< 0.1%
ValueCountFrequency (%)
456255 74
< 0.1%
456254 19
 
< 0.1%
456253 14
 
< 0.1%
456252 6
 
< 0.1%
456251 7
 
< 0.1%
456250 50
 
< 0.1%
456249 12
 
< 0.1%
456248 46
 
< 0.1%
456247 134
< 0.1%
456246 33
 
< 0.1%

NUM_INSTALMENT_VERSION
Real number (ℝ)

High correlation  Zeros 

Distinct65
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8566373
Minimum0
Maximum178
Zeros4082498
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size51.9 MiB
2025-03-25T05:13:04.634526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum178
Range178
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0352155
Coefficient of variation (CV)1.2084642
Kurtosis259.60709
Mean0.8566373
Median Absolute Deviation (MAD)0
Skewness9.5933943
Sum11654894
Variance1.0716711
MonotonicityNot monotonic
2025-03-25T05:13:04.925523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 8485004
62.4%
0 4082498
30.0%
2 620283
 
4.6%
3 237063
 
1.7%
4 55274
 
0.4%
5 48404
 
0.4%
6 17092
 
0.1%
7 16771
 
0.1%
9 8359
 
0.1%
8 7814
 
0.1%
Other values (55) 26839
 
0.2%
ValueCountFrequency (%)
0 4082498
30.0%
1 8485004
62.4%
2 620283
 
4.6%
3 237063
 
1.7%
4 55274
 
0.4%
5 48404
 
0.4%
6 17092
 
0.1%
7 16771
 
0.1%
8 7814
 
0.1%
9 8359
 
0.1%
ValueCountFrequency (%)
178 1
 
< 0.1%
73 1
 
< 0.1%
72 7
< 0.1%
68 1
 
< 0.1%
61 8
< 0.1%
59 1
 
< 0.1%
58 1
 
< 0.1%
57 1
 
< 0.1%
56 1
 
< 0.1%
55 1
 
< 0.1%

NUM_INSTALMENT_NUMBER
Real number (ℝ)

High correlation 

Distinct277
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.870896
Minimum1
Maximum277
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.0 MiB
2025-03-25T05:13:05.002711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median8
Q319
95-th percentile82
Maximum277
Range276
Interquartile range (IQR)15

Descriptive statistics

Standard deviation26.664067
Coefficient of variation (CV)1.412973
Kurtosis6.7051373
Mean18.870896
Median Absolute Deviation (MAD)5
Skewness2.4975965
Sum2.567461 × 108
Variance710.97246
MonotonicityNot monotonic
2025-03-25T05:13:05.074793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1004160
 
7.4%
2 985716
 
7.2%
3 968279
 
7.1%
4 943502
 
6.9%
5 880007
 
6.5%
6 827973
 
6.1%
7 679739
 
5.0%
8 644708
 
4.7%
9 592473
 
4.4%
10 549140
 
4.0%
Other values (267) 5529704
40.6%
ValueCountFrequency (%)
1 1004160
7.4%
2 985716
7.2%
3 968279
7.1%
4 943502
6.9%
5 880007
6.5%
6 827973
6.1%
7 679739
5.0%
8 644708
4.7%
9 592473
4.4%
10 549140
4.0%
ValueCountFrequency (%)
277 1
 
< 0.1%
276 1
 
< 0.1%
275 2
< 0.1%
274 1
 
< 0.1%
273 2
< 0.1%
272 2
< 0.1%
271 2
< 0.1%
270 2
< 0.1%
269 2
< 0.1%
268 3
< 0.1%

DAYS_INSTALMENT
Real number (ℝ)

High correlation 

Distinct2922
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1042.27
Minimum-2922
Maximum-1
Zeros0
Zeros (%)0.0%
Negative13605401
Negative (%)100.0%
Memory size51.9 MiB
2025-03-25T05:13:05.147269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-2922
5-th percentile-2553
Q1-1654
median-818
Q3-361
95-th percentile-81
Maximum-1
Range2921
Interquartile range (IQR)1293

Descriptive statistics

Standard deviation800.94617
Coefficient of variation (CV)-0.76846323
Kurtosis-0.79873717
Mean-1042.27
Median Absolute Deviation (MAD)557
Skewness-0.62870383
Sum-1.4180501 × 1010
Variance641514.75
MonotonicityNot monotonic
2025-03-25T05:13:05.222181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-120 11512
 
0.1%
-180 11212
 
0.1%
-150 11194
 
0.1%
-119 11183
 
0.1%
-149 11144
 
0.1%
-210 11140
 
0.1%
-90 11135
 
0.1%
-148 10922
 
0.1%
-179 10838
 
0.1%
-59 10828
 
0.1%
Other values (2912) 13494293
99.2%
ValueCountFrequency (%)
-2922 1327
< 0.1%
-2921 1436
< 0.1%
-2920 1458
< 0.1%
-2919 1485
< 0.1%
-2918 1454
< 0.1%
-2917 1443
< 0.1%
-2916 1391
< 0.1%
-2915 1422
< 0.1%
-2914 1462
< 0.1%
-2913 1522
< 0.1%
ValueCountFrequency (%)
-1 2
 
< 0.1%
-2 661
 
< 0.1%
-3 5031
< 0.1%
-4 6157
< 0.1%
-5 6122
< 0.1%
-6 6458
< 0.1%
-7 6865
0.1%
-8 6893
0.1%
-9 6593
< 0.1%
-10 7003
0.1%

DAYS_ENTRY_PAYMENT
Real number (ℝ)

High correlation 

Distinct3039
Distinct (%)< 0.1%
Missing2905
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-1051.1137
Minimum-4921
Maximum-1
Zeros0
Zeros (%)0.0%
Negative13602496
Negative (%)> 99.9%
Memory size51.9 MiB
2025-03-25T05:13:05.305245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-4921
5-th percentile-2561
Q1-1662
median-827
Q3-370
95-th percentile-90
Maximum-1
Range4920
Interquartile range (IQR)1292

Descriptive statistics

Standard deviation800.58618
Coefficient of variation (CV)-0.76165518
Kurtosis-0.80175561
Mean-1051.1137
Median Absolute Deviation (MAD)557
Skewness-0.62688923
Sum-1.429777 × 1010
Variance640938.25
MonotonicityNot monotonic
2025-03-25T05:13:05.382738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-91 13103
 
0.1%
-182 13090
 
0.1%
-154 13071
 
0.1%
-92 12646
 
0.1%
-245 12405
 
0.1%
-273 12151
 
0.1%
-119 11961
 
0.1%
-63 11938
 
0.1%
-153 11839
 
0.1%
-336 11839
 
0.1%
Other values (3029) 13478453
99.1%
ValueCountFrequency (%)
-4921 1
< 0.1%
-3160 1
< 0.1%
-3129 1
< 0.1%
-3115 1
< 0.1%
-3096 1
< 0.1%
-3082 1
< 0.1%
-3078 1
< 0.1%
-3071 1
< 0.1%
-3063 1
< 0.1%
-3061 1
< 0.1%
ValueCountFrequency (%)
-1 2
 
< 0.1%
-2 394
 
< 0.1%
-3 2610
< 0.1%
-4 3056
< 0.1%
-5 3049
< 0.1%
-6 3375
< 0.1%
-7 4309
< 0.1%
-8 4002
< 0.1%
-9 3748
< 0.1%
-10 4133
< 0.1%

AMT_INSTALMENT
Real number (ℝ)

High correlation 

Distinct902539
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17050.907
Minimum0
Maximum3771487.8
Zeros290
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size103.8 MiB
2025-03-25T05:13:05.458528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile188.145
Q14226.085
median8884.08
Q316710.21
95-th percentile47041.335
Maximum3771487.8
Range3771487.8
Interquartile range (IQR)12484.125

Descriptive statistics

Standard deviation50570.254
Coefficient of variation (CV)2.9658396
Kurtosis388.83939
Mean17050.907
Median Absolute Deviation (MAD)5509.08
Skewness16.235905
Sum2.3198443 × 1011
Variance2.5573506 × 109
MonotonicityNot monotonic
2025-03-25T05:13:05.539142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9000 254062
 
1.9%
2250 179120
 
1.3%
4500 174143
 
1.3%
6750 173659
 
1.3%
3375 149941
 
1.1%
5625 96362
 
0.7%
7875 60248
 
0.4%
1125 60224
 
0.4%
13500 42926
 
0.3%
8100 37295
 
0.3%
Other values (902529) 12377421
91.0%
ValueCountFrequency (%)
0 290
 
< 0.1%
0.045 2059
< 0.1%
0.09 1508
< 0.1%
0.135 1680
< 0.1%
0.18 1644
< 0.1%
0.225 1004
< 0.1%
0.27 1373
< 0.1%
0.315 1114
< 0.1%
0.36 1184
< 0.1%
0.405 899
< 0.1%
ValueCountFrequency (%)
3771487.845 1
< 0.1%
3473582.895 1
< 0.1%
3436835.13 1
< 0.1%
3371884.155 1
< 0.1%
3202061.805 1
< 0.1%
3199045.365 1
< 0.1%
3150949.545 1
< 0.1%
3116440.26 1
< 0.1%
3110086.215 1
< 0.1%
3094108.245 1
< 0.1%

AMT_PAYMENT
Real number (ℝ)

High correlation 

Distinct944235
Distinct (%)6.9%
Missing2905
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean17238.223
Minimum0
Maximum3771487.8
Zeros1440
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size103.8 MiB
2025-03-25T05:13:05.617356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile111.645
Q13398.265
median8125.515
Q316108.425
95-th percentile47732.175
Maximum3771487.8
Range3771487.8
Interquartile range (IQR)12710.16

Descriptive statistics

Standard deviation54735.784
Coefficient of variation (CV)3.1752567
Kurtosis324.59593
Mean17238.223
Median Absolute Deviation (MAD)5658.435
Skewness14.951925
Sum2.3448286 × 1011
Variance2.996006 × 109
MonotonicityNot monotonic
2025-03-25T05:13:05.696703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9000 248757
 
1.8%
2250 182654
 
1.3%
4500 178309
 
1.3%
6750 170360
 
1.3%
3375 141832
 
1.0%
5625 91165
 
0.7%
1125 64440
 
0.5%
7875 55823
 
0.4%
13500 46276
 
0.3%
8100 35271
 
0.3%
Other values (944225) 12387609
91.0%
ValueCountFrequency (%)
0 1440
 
< 0.1%
0.045 4176
< 0.1%
0.09 3057
< 0.1%
0.135 3095
< 0.1%
0.18 2837
< 0.1%
0.225 2050
< 0.1%
0.27 2534
< 0.1%
0.315 1982
< 0.1%
0.36 2244
< 0.1%
0.405 2021
< 0.1%
ValueCountFrequency (%)
3771487.845 1
< 0.1%
3473582.895 1
< 0.1%
3436835.13 1
< 0.1%
3371884.155 1
< 0.1%
3202061.805 1
< 0.1%
3199045.365 1
< 0.1%
3150949.545 1
< 0.1%
3116440.26 1
< 0.1%
3110086.215 1
< 0.1%
3094108.245 1
< 0.1%

Interactions

2025-03-25T05:12:29.607841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:11:06.093958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:11:17.968611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:11:29.895462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:11:41.829514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:11:53.437502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:12:05.571015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:12:18.079494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:12:31.161623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:11:07.572150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:11:19.328563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:11:31.274036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:11:43.222237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:11:54.892280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:12:07.166117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:12:19.472929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:12:32.713670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:11:09.015411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:11:20.748496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:11:32.584030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:11:44.623125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:11:56.404014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:12:08.771339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:12:20.864590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:12:34.234242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:11:10.408735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:11:22.168209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:11:33.924784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:11:45.985065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:11:57.841057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:12:10.355464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:12:22.247942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:12:35.780054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:11:11.839318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:11:23.655799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:11:35.551654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:11:47.375874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:11:59.236442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:12:11.941413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:12:23.648413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:12:37.333285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:11:13.428454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:11:25.315287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:11:37.075845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:11:48.967912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:12:00.859024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:12:13.454862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:12:25.183460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:12:38.890215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:11:14.903094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:11:26.766829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:11:38.642793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:11:50.403600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:12:02.322826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:12:15.084763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:12:26.517231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:12:40.316000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:11:16.511661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:11:28.447013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:11:40.377095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:11:51.980580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:12:03.936876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:12:16.696534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T05:12:28.065012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-03-25T05:13:05.754573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AMT_INSTALMENTAMT_PAYMENTDAYS_ENTRY_PAYMENTDAYS_INSTALMENTNUM_INSTALMENT_NUMBERNUM_INSTALMENT_VERSIONSK_ID_CURRSK_ID_PREV
AMT_INSTALMENT1.0000.9160.2250.228-0.3010.5210.0010.005
AMT_PAYMENT0.9161.0000.2220.225-0.3010.4850.0010.004
DAYS_ENTRY_PAYMENT0.2250.2221.0000.9990.0880.0780.0010.004
DAYS_INSTALMENT0.2280.2250.9991.0000.0830.0840.0010.004
NUM_INSTALMENT_NUMBER-0.301-0.3010.0880.0831.000-0.5410.001-0.001
NUM_INSTALMENT_VERSION0.5210.4850.0780.084-0.5411.000-0.0010.001
SK_ID_CURR0.0010.0010.0010.0010.001-0.0011.0000.002
SK_ID_PREV0.0050.0040.0040.004-0.0010.0010.0021.000

Missing values

2025-03-25T05:12:40.544328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-25T05:12:44.360844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-03-25T05:12:52.259965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

SK_ID_PREVSK_ID_CURRNUM_INSTALMENT_VERSIONNUM_INSTALMENT_NUMBERDAYS_INSTALMENTDAYS_ENTRY_PAYMENTAMT_INSTALMENTAMT_PAYMENT
010541861616741.06-1180.0-1187.06948.3606948.360
113308311516390.034-2156.0-2156.01716.5251716.525
220852311930532.01-63.0-63.025425.00025425.000
324525271996971.03-2418.0-2426.024350.13024350.130
427147241677561.02-1383.0-1366.02165.0402160.585
511373121644891.012-1384.0-1417.05970.3755970.375
622342641846934.011-349.0-352.029432.29529432.295
718185991114202.04-968.0-994.017862.16517862.165
827231831121020.014-197.0-197.070.74070.740
914139901097411.04-570.0-609.014308.47014308.470
SK_ID_PREVSK_ID_CURRNUM_INSTALMENT_VERSIONNUM_INSTALMENT_NUMBERDAYS_INSTALMENTDAYS_ENTRY_PAYMENTAMT_INSTALMENTAMT_PAYMENT
1360539114991954184866.032-112.0NaN48635.595NaN
1360539224488694343211.016-117.0NaN11504.250NaN
1360539312857364340690.073-853.0NaN67.500NaN
1360539416594434058410.0102-1888.0NaN67.500NaN
1360539514991954184866.02-1012.0NaN48635.595NaN
1360539621868574280570.066-1624.0NaN67.500NaN
1360539713103474144060.047-1539.0NaN67.500NaN
1360539813087664021990.043-7.0NaN43737.435NaN
1360539910622064092970.043-1986.0NaN67.500NaN
1360540024488694343211.019-27.0NaN11504.250NaN